OpenRaiser/Intern-Atlas huggingface dataset:
Lattice Coding for the Two-way Two-relay Channel
Venue: arXiv cs.IT 2012
Year: 2012

Lattice coding techniques may be used to derive achievable rate regions which outperform known independent, identically distributed (i.i.d.) random codes in multi-source relay networks and in particular the two-way relay channel. Gains stem from the ability to decode the sum of codewords (or messages) using lattice codes at higher rates than possible with i.i.d. random codes. Here we develop a novel lattice coding scheme for the Two-way Two-relay Channel: 1 <-> 2 <-> 3 <-> 4, where Node 1 and 4 simultaneously communicate with each other through two relay nodes 2 and 3. Each node only communicates with its neighboring nodes. The key technical contribution is the lattice-based achievability strategy, where each relay is able to remove the noise while decoding the sum of several signals in a Block Markov strategy and then re-encode the signal into another lattice codeword using the so-called "Re-distribution Transform". This allows nodes further down the line to again decode sums of lattice codewords. This transform is central to improving the achievable rates, and ensures that the messages traveling in each of the two directions fully utilize the relay's power, even under asymmetric channel conditions. All decoders are lattice decoders and only a single nested lattice codebook pair is needed. The symmetric rate achieved by the proposed lattice coding scheme is within 0.5 log 3 bit/Hz/s of the symmetric rate capacity.
Generating Strategic IS: Towards the Winning Strategy
Venue: arXiv cs.OH 2012
Year: 2012

In modern era, the role of information system in organization has been taken many discussions. The models of information system are constantly updated. However, most of them can not face the changing world. This paper discusses an approach to generating of strategic information system based on features in organization. We proposed an approach by using disadvantages in some tools of analysis whereby the lack of analysis appear as behaviour of relation between organisation and the world.
Convergence to Equilibrium of Logit Dynamics for Strategic Games
Venue: arXiv cs.GT 2012
Year: 2012

We present the first general bounds on the mixing time of the Markov chain associated to the logit dynamics for wide classes of strategic games. The logit dynamics with inverse noise beta describes the behavior of a complex system whose individual components act selfishly and keep responding according to some partial ("noisy") knowledge of the system, where the capacity of the agent to know the system and compute her best move is measured by the inverse of the parameter beta. In particular, we prove nearly tight bounds for potential games and games with dominant strategies. Our results show that, for potential games, the mixing time is upper and lower bounded by an exponential in the inverse of the noise and in the maximum potential difference. Instead, for games with dominant strategies, the mixing time cannot grow arbitrarily with the inverse of the noise. Finally, we refine our analysis for a subclass of potential games called graphical coordination games, a class of games that have been previously studied in Physics and, more recently, in Computer Science in the context of diffusion of new technologies. We give evidence that the mixing time of the logit dynamics for these games strongly depends on the structure of the underlying graph. We prove that the mixing time of the logit dynamics for these games can be upper bounded by a function that is exponential in the cutwidth of the underlying graph and in the inverse of noise. Moreover, we consider two specific and popular network topologies, the clique and the ring. For games played on a clique we prove an almost matching lower bound on the mixing time of the logit dynamics that is exponential in the inverse of the noise and in the maximum potential difference, while for games played on a ring we prove that the time of convergence of the logit dynamics to its stationary distribution is significantly shorter.
WorkingWiki: a MediaWiki-based platform for collaborative research
Venue: arXiv cs.HC 2012
Year: 2012

WorkingWiki is a software extension for the popular MediaWiki platform that makes a wiki into a powerful environment for collaborating on publication-quality manuscripts and software projects. Developed in Jonathan Dushoff's theoretical biology lab at McMaster University and available as free software, it allows wiki users to work together on anything that can be done by using UNIX commands to transform textual "source code" into output. Researchers can use it to collaborate on programs written in R, python, C, or any other language, and there are special features to support easy work on LaTeX documents. It develops the potential of the wiki medium to serve as a combination collaborative text editor, development environment, revision control system, and publishing platform. Its potential uses are open-ended - its processing is controlled by makefiles that are straightforward to customize - and its modular design is intended to allow parts of it to be adapted to other purposes.
A Survey on Information Retrieval, Text Categorization, and Web Crawling
Venue: arXiv cs.IR 2012
Year: 2012

This paper is a survey discussing Information Retrieval concepts, methods, and applications. It goes deep into the document and query modelling involved in IR systems, in addition to pre-processing operations such as removing stop words and searching by synonym techniques. The paper also tackles text categorization along with its application in neural networks and machine learning. Finally, the architecture of web crawlers is to be discussed shedding the light on how internet spiders index web documents and how they allow users to search for items on the web.
A Two Intermediates Audio Steganography Technique
Venue: arXiv cs.CR 2012
Year: 2012

On the rise of the Internet, digital data became openly public which has driven IT industries to pay special consideration to data confidentiality. At present, two main techniques are being used: Cryptography and Steganography. In effect, cryptography garbles a secret message turning it into a meaningless form; while, steganography hides the very existence of the message by embedding it into an intermediate such as a computer file. In fact, in audio steganography, this computer file is a digital audio file in which secret data are concealed, predominantly, into the bits that make up its audio samples. This paper proposes a novel steganography technique for hiding digital data into uncompressed audio files using a randomized algorithm and a context-free grammar coupled with a lexicon of words. Furthermore, the proposed technique uses two intermediates to transmit the secret data between communicating parties: The first intermediate is an audio file whose audio samples, which are selected randomly, are used to conceal the secret data; whereas, the second intermediate is a grammatically correct English text that is generated at runtime using a context-free grammar and it encodes the location of the random audio samples in the audio file. The proposed technique is stealthy and irrecoverable in a sense that it is difficult for unauthorized third parties to detect the presence of and recover the secret data. Experiments conducted showed how the covering and the uncovering processes of the proposed technique work. As future work, a semantic analyzer is to be developed so as to make the intermediate text not only grammatically correct but also semantically plausible.
Bag-of-Words Representation for Biomedical Time Series Classification
Venue: arXiv cs.LG 2012
Year: 2012

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.
Making Triangles Colorful
Venue: arXiv cs.CG 2012
Year: 2012

We prove that for any point set P in the plane, a triangle T, and a positive integer k, there exists a coloring of P with k colors such that any homothetic copy of T containing at least ck^8 points of P, for some constant c, contains at least one of each color. This is the first polynomial bound for range spaces induced by homothetic polygons. The only previously known bound for this problem applies to the more general case of octants in R^3, but is doubly exponential.
Model-order reduction of biochemical reaction networks
Venue: arXiv cs.SY 2012
Year: 2012

In this paper we propose a model-order reduction method for chemical reaction networks governed by general enzyme kinetics, including the mass-action and Michaelis-Menten kinetics. The model-order reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the graph structure of complexes in the chemical reaction network. We apply our method to a yeast glycolysis model, where the simulation result shows that the transient behaviour of a number of key metabolites of the reduced-order model is in good agreement with those of the full-order model.
On revising fuzzy belief bases
Venue: arXiv cs.AI 2012
Year: 2012

We look at the problem of revising fuzzy belief bases, i.e., belief base revision in which both formulas in the base as well as revision-input formulas can come attached with varying truth-degrees. Working within a very general framework for fuzzy logic which is able to capture a variety of types of inference under uncertainty, such as truth-functional fuzzy logics and certain types of probabilistic inference, we show how the idea of rational change from 'crisp' base revision, as embodied by the idea of partial meet revision, can be faithfully extended to revising fuzzy belief bases. We present and axiomatise an operation of partial meet fuzzy revision and illustrate how the operation works in several important special instances of the framework.
Parametric Dependability Analysis through Probabilistic Horn Abduction
Venue: arXiv cs.AI 2012
Year: 2012

Dependability modeling and evaluation is aimed at investigating that a system performs its function correctly in time. A usual way to achieve a high reliability, is to design redundant systems that contain several replicas of the same subsystem or component. State space methods for dependability analysis may suffer of the state space explosion problem in such a kind of situation. Combinatorial models, on the other hand, require the simplified assumption of statistical independence; however, in case of redundant systems, this does not guarantee a reduced number of modeled elements. In order to provide a more compact system representation, parametric system modeling has been investigated in the literature, in such a way that a set of replicas of a given subsystem is parameterized so that only one representative instance is explicitly included. While modeling aspects can be suitably addressed by these approaches, analytical tools working on parametric characterizations are often more difficult to be defined and the standard approach is to 'unfold' the parametric model, in order to exploit standard analysis algorithms working at the unfolded 'ground' level. Moreover, parameterized combinatorial methods still require the statistical independence assumption. In the present paper we consider the formalism of Parametric Fault Tree (PFT) and we show how it can be related to Probabilistic Horn Abduction (PHA). Since PHA is a framework where both modeling and analysis can be performed in a restricted first-order language, we aim at showing that converting a PFT into a PHA knowledge base will allow an approach to dependability analysis directly exploiting parametric representation. We will show that classical qualitative and quantitative dependability measures can be characterized within PHA. Furthermore, additional modeling aspects (such as noisy gates and local dependencies) as well as additional reliability measures (such as posterior probability analysis) can be naturally addressed by this conversion. A simple example of a multi-processor system with several replicated units is used to illustrate the approach.
Bayesian Hierarchical Mixtures of Experts
Venue: arXiv cs.LG 2012
Year: 2012

The Hierarchical Mixture of Experts (HME) is a well-known tree-based model for regression and classification, based on soft probabilistic splits. In its original formulation it was trained by maximum likelihood, and is therefore prone to over-fitting. Furthermore the maximum likelihood framework offers no natural metric for optimizing the complexity and structure of the tree. Previous attempts to provide a Bayesian treatment of the HME model have relied either on ad-hoc local Gaussian approximations or have dealt with related models representing the joint distribution of both input and output variables. In this paper we describe a fully Bayesian treatment of the HME model based on variational inference. By combining local and global variational methods we obtain a rigourous lower bound on the marginal probability of the data under the model. This bound is optimized during the training phase, and its resulting value can be used for model order selection. We present results using this approach for a data set describing robot arm kinematics.
An Empirical Study of w-Cutset Sampling for Bayesian Networks
Venue: arXiv cs.AI 2012
Year: 2012

The paper studies empirically the time-space trade-off between sampling and inference in a sl cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. Consequently, while the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. The w-cutset sampling selects a sampling set such that the induced-width of the network when the sampling set is observed is bounded by w, thus requiring inference whose complexity is exponential in w. In this paper, we investigate performance of w-cutset sampling over a range of w values and measure the accuracy of w-cutset sampling as a function of w. Our experiments demonstrate that the cutset sampling idea is quite powerful showing that an optimal balance between inference and sampling benefits substantially from restricting the cutset size, even at the cost of more complex inference.
Reasoning about Bayesian Network Classifiers
Venue: arXiv cs.LG 2012
Year: 2012

Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size even given an intractable number of instances. Since ODDs are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.
Efficient Gradient Estimation for Motor Control Learning
Venue: arXiv cs.LG 2012
Year: 2012

The task of estimating the gradient of a function in the presence of noise is central to several forms of reinforcement learning, including policy search methods. We present two techniques for reducing gradient estimation errors in the presence of observable input noise applied to the control signal. The first method extends the idea of a reinforcement baseline by fitting a local linear model to the function whose gradient is being estimated; we show how to find the linear model that minimizes the variance of the gradient estimate, and how to estimate the model from data. The second method improves this further by discounting components of the gradient vector that have high variance. These methods are applied to the problem of motor control learning, where actuator noise has a significant influence on behavior. In particular, we apply the techniques to learn locally optimal controllers for a dart-throwing task using a simulated three-link arm; we demonstrate that proposed methods significantly improve the reward function gradient estimate and, consequently, the learning curve, over existing methods.
Monte-Carlo optimizations for resource allocation problems in stochastic network systems
Venue: arXiv cs.AI 2012
Year: 2012

Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of optimization tasks performed routinely upon such systems, in particular, various resource allocation tasks. In this work we investigate and develop Monte Carlo solutions for a class of two-stage optimization problems in stochastic networks in which the expected value of resource allocations before and after stochastic failures needs to be optimized. The limitation of these problems is that their exact solutions are exponential in the number of unreliable network components: thus, exact methods do not scale-up well to large networks often seen in practice. We first prove that Monte Carlo optimization methods can overcome the exponential bottleneck of exact methods. Next we support our theoretical findings on resource allocation experiments and show a very good scale-up potential of the new methods to large stochastic networks.
Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
Venue: arXiv cs.AI 2012
Year: 2012

This paper examines a number of solution methods for decision processes with non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable to well-known MDP solution methods. They differ however in the representation of the target MDP and the class of MDP solution methods to which they are suited. As a result, they adopt different temporal logics and different translations. Unfortunately, no implementation of these methods nor experimental let alone comparative results have ever been reported. This paper is the first step towards filling this gap. We describe an integrated system for solving NMRDPs which implements these methods and several variants under a common interface; we use it to compare the various approaches and identify the problem features favoring one over the other.
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
Venue: arXiv cs.AI 2012
Year: 2012

There has been great interest in identifying tractable subclasses of NP complete problems and designing efficient algorithms for these tractable classes. Constraint satisfaction and Bayesian network inference are two examples of such problems that are of great importance in AI and algorithms. In this paper we study, under the frameworks of random constraint satisfaction problems and random Bayesian networks, a typical tractable subclass characterized by the treewidth of the problems. We show that the property of having a bounded treewidth for CSPs and Bayesian network inference problem has a phase transition that occurs while the underlying structures of problems are still sparse. This implies that algorithms making use of treewidth based structural knowledge only work efficiently in a limited range of random instance.
Marginalizing Out Future Passengers in Group Elevator Control
Venue: arXiv cs.AI 2012
Year: 2012

Group elevator scheduling is an NP-hard sequential decision-making problem with unbounded state spaces and substantial uncertainty. Decision-theoretic reasoning plays a surprisingly limited role in fielded systems. A new opportunity for probabilistic methods has opened with the recent discovery of a tractable solution for the expected waiting times of all passengers in the building, marginalized over all possible passenger itineraries. Though commercially competitive, this solution does not contemplate future passengers. Yet in up-peak traffic, the effects of future passengers arriving at the lobby and entering elevator cars can dominate all waiting times. We develop a probabilistic model of how these arrivals affect the behavior of elevator cars at the lobby, and demonstrate how this model can be used to very significantly reduce the average waiting time of all passengers.
Efficient Parametric Projection Pursuit Density Estimation
Venue: arXiv cs.LG 2012
Year: 2012

Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert models a one dimensional projection of the data. The UPoE is fully tractable and may be interpreted as a parametric probabilistic model for projection pursuit. Its ML learning rules are identical to the approximate learning rules proposed before for under-complete ICA. We also derive an efficient sequential learning algorithm and discuss its relationship to projection pursuit density estimation and feature induction algorithms for additive random field models.
The effect of the number of mobile nodes on varying speeds of manets
Venue: arXiv cs.NI 2012
Year: 2012

Mobile Ad hoc Networks are dynamic networks populated by mobile devices, or mobile nodes.The Mobile Nodes are free to move anywhere and at any time. The population of the nodes may have some influence on the mobility rate of the mobile nodes. This paper presents simulation results using Matrix Laboratory software. The study investigates the influence of mobile nodes parameters such as number of nodes on the nodes speeds and nodes distribution in a given area. The results have indicated that the number of mobile nodes have impact on the speeds of the nodes in a location.